

# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowercase			:				Union[str, Any]   =     2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowercase			:				Optional[int]   =     {
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
lowercase			:				Tuple   =     {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		lowercase			:				Any   =     "facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
		lowercase			:				Any   =     "allenai"






def        snake_case__		(    lowerCamelCase_		):
							# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
							# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
							A      :					Any      		=					dict((re.sub(r'''@@$'''     ,				''''''     ,				lowerCamelCase_		), v) if k.endswith('''@@'''		) else (re.sub(r'''$'''     ,				'''</w>'''     ,				lowerCamelCase_		), v) for k, v in d.items()		)
							A      :					Optional[Any]      		=					'''<s> <pad> </s> <unk>'''.split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'{k}</w>']
														A      :					Union[str, Any]      		=					d[k]  # restore
							return da






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							# prep
							assert os.path.exists(lowerCamelCase_		)
							os.makedirs(lowerCamelCase_     ,				exist_ok=lowerCamelCase_		)
							print(F'Writing results to {pytorch_dump_folder_path}'		)

							# handle various types of models

							A      :					Optional[Any]      		=					basename(lowerCamelCase_		)
							A      :					List[Any]      		=					dirname(lowerCamelCase_		)

							A      :					str      		=					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
							A      :					Dict      		=					cls.hub_models()
							A      :					Union[str, Any]      		=					{'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
							A      :					Optional[int]      		=					'''.'''
							# note: since the model dump is old, fairseq has upgraded its model some
							# time later, and it does a whole lot of rewrites and splits on the saved
							# weights, therefore we can't use torch.load() directly on the model file.
							# see: upgrade_state_dict(state_dict) in fairseq_model.py
							print(F'using checkpoint {checkpoint_file}'		)
							A      :					Optional[int]      		=					hub_utils.from_pretrained(
							    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				archive_map=lowerCamelCase_     ,				**lowerCamelCase_		)

							A      :					Optional[Any]      		=					vars(chkpt['''args''']['''model''']		)

							A      :					Union[str, Any]      		=					args['''source_lang''']
							A      :					str      		=					args['''target_lang''']

							A      :					Tuple      		=					dirname(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					basename(lowerCamelCase_		)

							# dicts
							A      :					Optional[int]      		=					os.path.join(lowerCamelCase_     ,				F'dict.{src_lang}.txt'		)
							A      :					int      		=					os.path.join(lowerCamelCase_     ,				F'dict.{tgt_lang}.txt'		)

							A      :					Tuple      		=					Dictionary.load(lowerCamelCase_		)
							A      :					Dict      		=					rewrite_dict_keys(src_dict.indices		)
							A      :					Optional[Any]      		=					len(lowerCamelCase_		)
							A      :					Optional[Any]      		=					os.path.join(lowerCamelCase_     ,				'''vocab-src.json'''		)
							print(F'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'		)
							with open(lowerCamelCase_     ,				'''w'''     ,				encoding='''utf-8'''		) as f:
														f.write(json.dumps(lowerCamelCase_     ,				ensure_ascii=lowerCamelCase_     ,				indent=lowerCamelCase_		)		)

							# detect whether this is a do_lower_case situation, which can be derived by checking whether we
							# have at least one uppercase letter in the source vocab
							A      :					List[str]      		=					True
							for k in src_vocab.keys():
														if not k.islower():
																					A      :					List[str]      		=					False
																					break

							A      :					Dict      		=					Dictionary.load(lowerCamelCase_		)
							A      :					int      		=					rewrite_dict_keys(tgt_dict.indices		)
							A      :					Optional[Any]      		=					len(lowerCamelCase_		)
							A      :					Optional[int]      		=					os.path.join(lowerCamelCase_     ,				'''vocab-tgt.json'''		)
							print(F'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'		)
							with open(lowerCamelCase_     ,				'''w'''     ,				encoding='''utf-8'''		) as f:
														f.write(json.dumps(lowerCamelCase_     ,				ensure_ascii=lowerCamelCase_     ,				indent=lowerCamelCase_		)		)

							# merges_file (bpecodes)
							A      :					str      		=					os.path.join(lowerCamelCase_     ,				VOCAB_FILES_NAMES['''merges_file''']		)
							for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
														A      :					List[Any]      		=					os.path.join(lowerCamelCase_     ,				lowerCamelCase_		)
														if os.path.exists(lowerCamelCase_		):
																					break
							with open(lowerCamelCase_     ,				encoding='''utf-8'''		) as fin:
														A      :					int      		=					fin.read()
							A      :					Tuple      		=					re.sub(r''' \d+$'''     ,				''''''     ,				lowerCamelCase_     ,				0     ,				re.M		)  # remove frequency number
							print(F'Generating {merges_file}'		)
							with open(lowerCamelCase_     ,				'''w'''     ,				encoding='''utf-8'''		) as fout:
														fout.write(lowerCamelCase_		)

							# model config
							A      :					Union[str, Any]      		=					os.path.join(lowerCamelCase_     ,				'''config.json'''		)

							# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
							# may have to modify the tokenizer if a different type is used by a future model
							assert args["bpe"] == "fastbpe", F'need to extend tokenizer to support bpe={args["bpe"]}'
							assert args["tokenizer"] == "moses", F'need to extend tokenizer to support bpe={args["tokenizer"]}'

							A      :					Optional[Any]      		=					{
							    '''architectures''': ['''FSMTForConditionalGeneration'''],
							    '''model_type''': '''fsmt''',
							    '''activation_dropout''': args['''activation_dropout'''],
							    '''activation_function''': '''relu''',
							    '''attention_dropout''': args['''attention_dropout'''],
							    '''d_model''': args['''decoder_embed_dim'''],
							    '''dropout''': args['''dropout'''],
							    '''init_std''': 0.02,
							    '''max_position_embeddings''': args['''max_source_positions'''],
							    '''num_hidden_layers''': args['''encoder_layers'''],
							    '''src_vocab_size''': src_vocab_size,
							    '''tgt_vocab_size''': tgt_vocab_size,
							    '''langs''': [src_lang, tgt_lang],
							    '''encoder_attention_heads''': args['''encoder_attention_heads'''],
							    '''encoder_ffn_dim''': args['''encoder_ffn_embed_dim'''],
							    '''encoder_layerdrop''': args['''encoder_layerdrop'''],
							    '''encoder_layers''': args['''encoder_layers'''],
							    '''decoder_attention_heads''': args['''decoder_attention_heads'''],
							    '''decoder_ffn_dim''': args['''decoder_ffn_embed_dim'''],
							    '''decoder_layerdrop''': args['''decoder_layerdrop'''],
							    '''decoder_layers''': args['''decoder_layers'''],
							    '''bos_token_id''': 0,
							    '''pad_token_id''': 1,
							    '''eos_token_id''': 2,
							    '''is_encoder_decoder''': True,
							    '''scale_embedding''': not args['''no_scale_embedding'''],
							    '''tie_word_embeddings''': args['''share_all_embeddings'''],
							}

							# good hparam defaults to start with
							A      :					Union[str, Any]      		=					5
							A      :					Tuple      		=					False
							if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
														A      :					Optional[int]      		=					best_score_hparams[model_dir]['''length_penalty''']
							else:
														A      :					List[Any]      		=					1.0

							print(F'Generating {fsmt_model_config_file}'		)
							with open(lowerCamelCase_     ,				'''w'''     ,				encoding='''utf-8'''		) as f:
														f.write(json.dumps(lowerCamelCase_     ,				ensure_ascii=lowerCamelCase_     ,				indent=lowerCamelCase_		)		)

							# tokenizer config
							A      :					Optional[int]      		=					os.path.join(lowerCamelCase_     ,				lowerCamelCase_		)

							A      :					Union[str, Any]      		=					{
							    '''langs''': [src_lang, tgt_lang],
							    '''model_max_length''': 1024,
							    '''do_lower_case''': do_lower_case,
							}

							print(F'Generating {fsmt_tokenizer_config_file}'		)
							with open(lowerCamelCase_     ,				'''w'''     ,				encoding='''utf-8'''		) as f:
														f.write(json.dumps(lowerCamelCase_     ,				ensure_ascii=lowerCamelCase_     ,				indent=lowerCamelCase_		)		)

							# model
							A      :					str      		=					chkpt['''models'''][0]
							A      :					int      		=					model.state_dict()

							# rename keys to start with 'model.'
							A      :					Union[str, Any]      		=					OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()		)

							# remove unneeded keys
							A      :					List[str]      		=					[
							    '''model.model''',
							    '''model.encoder.version''',
							    '''model.decoder.version''',
							    '''model.encoder_embed_tokens.weight''',
							    '''model.decoder_embed_tokens.weight''',
							    '''model.encoder.embed_positions._float_tensor''',
							    '''model.decoder.embed_positions._float_tensor''',
							]
							for k in ignore_keys:
														model_state_dict.pop(lowerCamelCase_     ,				lowerCamelCase_		)

							A      :					List[str]      		=					FSMTConfig.from_pretrained(lowerCamelCase_		)
							A      :					Any      		=					FSMTForConditionalGeneration(lowerCamelCase_		)

							# check that it loads ok
							model_new.load_state_dict(lowerCamelCase_     ,				strict=lowerCamelCase_		)

							# save
							A      :					Dict      		=					os.path.join(lowerCamelCase_     ,				lowerCamelCase_		)
							print(F'Generating {pytorch_weights_dump_path}'		)
							torch.save(lowerCamelCase_     ,				lowerCamelCase_		)

							print('''Conversion is done!'''		)
							print('''\nLast step is to upload the files to s3'''		)
							print(F'cd {data_root}'		)
							print(F'transformers-cli upload {model_dir}'		)


if __name__ == "__main__":
		lowercase			:				Dict   =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--fsmt_checkpoint_path",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
		        " bpecodes, etc."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowercase			:				Dict   =     parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)









import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowercase			:				Dict   =     logging.get_logger(__name__)

lowercase			:				Dict   =     OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

lowercase			:				Tuple   =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def        snake_case__		(    lowerCamelCase_		):
							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					A      :					Optional[Any]      		=					model_type_to_module_name(lowerCamelCase_		)

																					A      :					Optional[int]      		=					importlib.import_module(F'.{module_name}'     ,				'''transformers.models'''		)
																					try:
																												return getattr(lowerCamelCase_     ,				lowerCamelCase_		)
																					except AttributeError:
																												continue

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(lowerCamelCase_     ,				'''__name__'''     ,				lowerCamelCase_		) == class_name:
																					return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
							A      :					Any      		=					importlib.import_module('''transformers'''		)
							if hasattr(lowerCamelCase_     ,				lowerCamelCase_		):
														return getattr(lowerCamelCase_     ,				lowerCamelCase_		)

							return None






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_ = None     ,				lowerCamelCase_ = False     ,				lowerCamelCase_ = False     ,				lowerCamelCase_ = None     ,				lowerCamelCase_ = None     ,				lowerCamelCase_ = None     ,				lowerCamelCase_ = False     ,				**lowerCamelCase_     ,				):
							A      :					Optional[Any]      		=					get_file_from_repo(
							    lowerCamelCase_     ,				lowerCamelCase_     ,				cache_dir=lowerCamelCase_     ,				force_download=lowerCamelCase_     ,				resume_download=lowerCamelCase_     ,				proxies=lowerCamelCase_     ,				use_auth_token=lowerCamelCase_     ,				revision=lowerCamelCase_     ,				local_files_only=lowerCamelCase_     ,				)
							if resolved_config_file is None:
														logger.info(
														    '''Could not locate the image processor configuration file, will try to use the model config instead.'''		)
														return {}

							with open(lowerCamelCase_     ,				encoding='''utf-8'''		) as reader:
														return json.load(lowerCamelCase_		)







class 					__lowercase  :





							"""simple docstring"""

							def __init__(					self						)		->       int:
														raise EnvironmentError(
														    '''AutoImageProcessor is designed to be instantiated '''
														    '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''						)




							@classmethod
							@replace_list_option_in_docstrings(__UpperCAmelCase						)
							def       snake_case			(					cls    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Tuple:
														A      :					Tuple      		=					kwargs.pop('''config'''    ,						__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					kwargs.pop('''trust_remote_code'''    ,						__UpperCAmelCase						)
														A      :					str      		=					True

														A		,		A      :					str      		=					ImageProcessingMixin.get_image_processor_dict(__UpperCAmelCase    ,						**__UpperCAmelCase						)
														A      :					List[Any]      		=					config_dict.get('''image_processor_type'''    ,						__UpperCAmelCase						)
														A      :					List[Any]      		=					None
														if "AutoImageProcessor" in config_dict.get('''auto_map'''    ,						{}						):
																					A      :					List[str]      		=					config_dict['''auto_map''']['''AutoImageProcessor''']

														# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
														# and if so, infer the image processor class from there.
														if image_processor_class is None and image_processor_auto_map is None:
																					A      :					List[str]      		=					config_dict.pop('''feature_extractor_type'''    ,						__UpperCAmelCase						)
																					if feature_extractor_class is not None:
																												logger.warning(
																												    '''Could not find image processor class in the image processor config or the model config. Loading'''
																												    ''' based on pattern matching with the model\'s feature extractor configuration.'''						)
																												A      :					Tuple      		=					feature_extractor_class.replace('''FeatureExtractor'''    ,						'''ImageProcessor'''						)
																					if "AutoFeatureExtractor" in config_dict.get('''auto_map'''    ,						{}						):
																												A      :					Any      		=					config_dict['''auto_map''']['''AutoFeatureExtractor''']
																												A      :					int      		=					feature_extractor_auto_map.replace('''FeatureExtractor'''    ,						'''ImageProcessor'''						)
																												logger.warning(
																												    '''Could not find image processor auto map in the image processor config or the model config.'''
																												    ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''						)

        # If we don't find the image processor class in the image processor config, let's try the model config.
														if image_processor_class is None and image_processor_auto_map is None:
																					if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																												A      :					List[str]      		=					AutoConfig.from_pretrained(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					# It could be in `config.image_processor_type``
																					A      :					Any      		=					getattr(__UpperCAmelCase    ,						'''image_processor_type'''    ,						__UpperCAmelCase						)
																					if hasattr(__UpperCAmelCase    ,						'''auto_map'''						) and "AutoImageProcessor" in config.auto_map:
																												A      :					List[Any]      		=					config.auto_map['''AutoImageProcessor''']

														if image_processor_class is not None:
																					A      :					str      		=					image_processor_class_from_name(__UpperCAmelCase						)

														A      :					int      		=					image_processor_auto_map is not None
														A      :					List[str]      		=					image_processor_class is not None or type(__UpperCAmelCase						) in IMAGE_PROCESSOR_MAPPING
														A      :					Optional[Any]      		=					resolve_trust_remote_code(
														    __UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)

														if has_remote_code and trust_remote_code:
																					A      :					Tuple      		=					get_class_from_dynamic_module(
																					    __UpperCAmelCase    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					Optional[Any]      		=					kwargs.pop('''code_revision'''    ,						__UpperCAmelCase						)
																					if os.path.isdir(__UpperCAmelCase						):
																												image_processor_class.register_for_auto_class()
																					return image_processor_class.from_dict(__UpperCAmelCase    ,						**__UpperCAmelCase						)
														elif image_processor_class is not None:
																					return image_processor_class.from_dict(__UpperCAmelCase    ,						**__UpperCAmelCase						)
														# Last try: we use the IMAGE_PROCESSOR_MAPPING.
														elif type(__UpperCAmelCase						) in IMAGE_PROCESSOR_MAPPING:
																					A      :					Tuple      		=					IMAGE_PROCESSOR_MAPPING[type(__UpperCAmelCase						)]
																					return image_processor_class.from_dict(__UpperCAmelCase    ,						**__UpperCAmelCase						)

														raise ValueError(
														    f'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
														    f'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
														    f'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()						)}'						)






							@staticmethod
							def       snake_case			(					__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:
														IMAGE_PROCESSOR_MAPPING.register(__UpperCAmelCase    ,						__UpperCAmelCase						)






